arXiv research

The paper learns particle swarming models from data using Gaussian processes.

Paper uses averaging from many particle filters to approximate posterior predictive distributions.

The paper connects PSO and CBO methods using stochastic modeling and mean-field limits.

New algorithm uses PSO to optimize DNN training parameters in distributed systems.

As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Opt…

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based alg…

A new optimizer, MVO, improves nonlinear regression performance.

AdaSwarm optimizes deep learning models with swarm intelligence, outperforming Adam.

PSO optimizes model parameters in nonstandard distributions.

Empirical comparison of 18 hyperparameter tuning algorithms for SVM.

A new time-series clustering method using slope-based similarity and PSO.

mFI-PSO generates effective adversarial images for DNNs.

Supervised classification is the most active and emerging research trends in today's scenario. In this view, Artificial Neural Network (ANN) techniques have been widely employed and growing interest to the researchers day by day. ANN training aims to find the proper setting of parameters such as weights ($\textbf{W}$) …

Optimal income crossover found using particle swarm optimization.

Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.

Metaheuristics improve yield curve estimation for Costa Rica.

Derives a fluid model for fish swarming in arbitrary dimensions.

Method for explaining machine learning survival models using counterfactuals.

Study defines and optimizes bank reliability using LR and PSO.

Gradient Descent (GD) approximators often fail in the solution space with multiple scales of convexities, i.e., in subspace learning and neural network scenarios. To handle that, one solution is to run GD multiple times from different randomized initial states and select the best solution over all experiments. However,…

Optimizes trading portfolios considering risk and profit.

ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.

PSO improves $G$-optimal designs for up to 5 factors, reducing computation time.

This paper improves combine harvester performance using ANN-PSO hybrid model.

Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs. These adversarial examples are created by applying adversarial perturbations to input samples, which would cause them to be misclassified by the target models. Attacks that search…

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data coul…

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, …

Transformers approximate mean-field dynamics of indistinguishable particles.

Novel LSTM network predicts pulsar timing residuals with few-shot data.

Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a non-parametric statistical learning approach to estimate the governing laws of distance-based interactions, with no reference or a…

Method identifies IPS governing equations from particle data efficiently.

Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimisation methods. Inspired by the success of Deep Neuroevolution in reinforcement learning (Such et al. 2017), we explore the use of gradient-…

Optimizes bus schedules to improve on-time performance.

PSO optimizes hyperparameters for edge ML models in FL.

DBS uses swarm intelligence to cluster data without needing a global objective function.

A combination of a priority queueing model and mean field theory shows the emergence of traders' swarm behavior, even when each has a subjective prediction of the market driven by a limit order book. Using a nonlinear Markov model, we analyze the dynamics of traders who select a favorable order price taking into accoun…

Paper proposes a federated learning framework for UAV swarms, optimizing convergence rate and energy consumption.

Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influenc…

We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare …

In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swar…

We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e.g., swarming) with mild communication requirements. Specifically, we analyze a scheme in which each one of $N > 1$ independent threads, implements in a distributed and unsynchronized fashion,…

Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with…

Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…

Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a ra…

Proposes a new framework for predicting stock market movements using sparse neural architectures.

This paper reviews feature selection methods using swarm intelligence.

Randomized feature models learn interaction kernels from agent paths.

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming b…